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Toolbox or Adjustable Spanner? A Critical Comparison of Two Metaphors for Adaptive Decision Making

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Anke Söllner at University of Mannheim
Anke Söllner
Arndt Bröder at University of Mannheim
Arndt Bröder
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Abstract

For multiattribute decision tasks, different metaphors exist that describe the process of decision making and its adaptation to diverse problems and situations. Multiple strategy models (MSMs) assume that decision makers choose adaptively from a set of different strategies (toolbox metaphor), whereas evidence accumulation models (EAMs) hold that a uniform mechanism is employed but is adapted to the environmental change (adjustable spanner metaphor). Despite recent claims that the frameworks are hard to disentangle empirically, both metaphors make distinct predictions concerning the information acquisition behavior, namely, that search is terminated according to the selected strategy (MSMs) or that information is acquired until an evidence threshold is passed (EAMs). In 3 experiments, we contrasted these predictions by providing participants with different degrees of evidence in a half-open/half-closed information board. For the majority of participants, we find that their stopping behavior is well captured by the notion of an evidence threshold that is either undercut or passed by the given evidence. (PsycINFO Database Record
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Toolbox or Adjustable Spanner? A Critical Comparison of Two
Metaphors for Adaptive Decision Making
Anke Söllner and Arndt Bröder
University of Mannheim
For multiattribute decision tasks, different metaphors exist that describe the process of decision making
and its adaptation to diverse problems and situations. Multiple strategy models (MSMs) assume that
decision makers choose adaptively from a set of different strategies (toolbox metaphor), whereas
evidence accumulation models (EAMs) hold that a uniform mechanism is employed but is adapted to the
environmental change (adjustable spanner metaphor). Despite recent claims that the frameworks are hard
to disentangle empirically, both metaphors make distinct predictions concerning the information acqui-
sition behavior, namely, that search is terminated according to the selected strategy (MSMs) or that
information is acquired until an evidence threshold is passed (EAMs). In 3 experiments, we contrasted
these predictions by providing participants with different degrees of evidence in a half-open/half-closed
information board. For the majority of participants, we find that their stopping behavior is well captured
by the notion of an evidence threshold that is either undercut or passed by the given evidence.
Keywords: decision making, evidence accumulation, information board, single-process models,
multiple-strategy models
Supplemental materials: http://dx.doi.org/10.1037/xlm0000162.supp
When choosing between multiple options, decision makers
sometimes rely on one good reason only, and sometimes they
search for a lot of arguments before making their decision. Ob-
serving these adaptations, one can conclude that humans employ
different decision strategies in different situations (toolbox meta-
phor; e.g., Gigerenzer, Todd, & the ABC Research Group, 1999;
Payne, Bettman, & Johnson, 1993). But the behavioral changes
can also be explained by assuming that a uniform mechanism is
used, with its parameters adapted to the situation at hands (adjust-
able spanner metaphor; e.g., Lee & Cummins, 2004;Newell,
2005). These two metaphors or frameworks of decision making
coexist, primarily because they are both able to account for the vast
majority of empirical findings, but also because they are hard to
disentangle empirically (Jekel, 2012;Newell, 2005;Newell &
Bröder, 2008).
1
In the current article, we concentrate on predic-
tions from the two frameworks concerning the termination of
information acquisition and contrast them in a novel paradigm that
systematically varies the level of given evidence.
The remainder of this introduction is organized as follows: First,
we introduce the aforementioned two frameworks of decision
making in more detail. We then address the question of why
disentangling these coexisting approaches poses an “empirical
challenge” (Newell, 2005, p. 13) and give a brief overview of
recent attempts to tackle this task. Finally, we introduce a novel
paradigm that enables us to contrast the two frameworks by
concentrating on their predictions concerning the termination of
information acquisition under varying levels of given evidence.
This paradigm constitutes the basis for the three experiments
reported and discussed in the remainder of this article.
Two Frameworks of Decision Making
The two frameworks we will describe in turn address multiat-
tribute decision tasks. Here, among two or more options (e.g.,
potential oil drilling sites), decision makers have to choose the one
that scores highest on a certain criterion (e.g., quantity of contained
oil). As decision aids, attributes (or cues) that evaluate the options
can be consulted (e.g., a chemical analysis yielding a positive or
negative evaluation), and each cue has some validity in reference
to the decision criterion (e.g., a validity of .80 means that in eight
of 10 cases in which the chemical analysis discriminates, it favors
the site that actually contains the most oil). If the criterion is an
objective one (e.g., the quantity of oil), the task is referred to as
probabilistic inference, whereas a subjective criterion (e.g., pref-
erence for a day trip) characterizes a preferential choice task.As
empirical similarities suggest similar cognitive processes in both
domains (Bröder & Newell, 2008;Payne et al., 1993;Todd,
1
In this article, we focus on descriptive analyses, that is, the question of
whether empirical data are well accounted for by different models. Pre-
scriptive analyses, in contrast, concentrate on the question whether a
process complies with a normative standard, for example, whether a
specific decision strategy leads to a high percentage of correct decisions
given a certain environmental structure (e.g., Czerlinski, Gigerenzer, &
Goldstein, 1999;Gigerenzer & Brighton, 2009;Hogarth & Karelaia, 2006)
or under which environmental regularities limited search is justified (Lee &
Zhang, 2012).
This article was published Online First August 10, 2015.
Anke Söllner and Arndt Bröder, School of Social Sciences, University of
Mannheim.
The authors would like to thank Tilmann Betsch, Andreas Glöckner, and
Benjamin Hilbig for helpful comments on earlier drafts of this article.
Correspondence concerning this article should be addressed to Anke
Söllner, University of Mannheim, L13, 17, D-68131 Mannheim, Germany.
E-mail: anke.soellner@uni-mannheim.de
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Experimental Psychology:
Learning, Memory, and Cognition © 2015 American Psychological Association
2016, Vol. 42, No. 2, 215–237 0278-7393/16/$12.00 http://dx.doi.org/10.1037/xlm0000162
215
... Das erste Ziel der konzeptuellen Replikationsstudie besteht in der Replikation der für die EA Modelle sprechenden Befunde von Söllner und Bröder (2016). Das zweite Ziel besteht in einer Manipulation der Reizumgebung durch Informationskosten, um eine adaptive Verwendung von Entscheidungsstrategien zu erzeugen und zu untersuchen, indem die Variation der Informationskosten Within-Subject, statt Between-Subject (Söllner & Bröder, 2016) implementiert wurde. Das dritte Ziel besteht in einer Erweiterung der Ergebnisse von Söllner und Bröder (2016) (Newell, 2005). ...
... Die Ausdifferenzierung und empirische Testung der beiden Konzeptualisierungen von Entscheidungsprozessen ist eine der großen aktuellen Herausforderungen der empirischen Entscheidungsforschung (e.g. Krefeld-Schwalb et al., 2019;Lee et al., 2014;Marewski et al., 2018;Newell, 2005;Söllner & Bröder, 2016). ...
... Die Besonderheit der beiden Paradigmen besteht darin, dass diese sich nachahmen können (Krefeld-Schwalb et al., 2019, Lee et al., 2014Newell, 2005;Söllner & Bröder, 2016). Um die Nachahmung der Paradigmen zu untersuchen, wurde analog zu Söllner & Bröder (2016) (Bröder, 2010). ...
Adaptive toolbox or adjustable spanner? An empirical test of the two paradigms of decision research through a conceptual replication study (Söllner & Bröder, 2016) in connection with strategy selection, information search and confidence in decisions
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  • Jul 2020
In current decision research in connection with probabilistic inference tasks, two competing paradigms have emerged. Multiple strategy models (MSM) assume that people have different decision strategies in an adaptive toolbox that are used in an ecologically rational way in connection with environmental conditions to make decisions. Evidence accumulation models (EAM), on the other hand, assume that people make decisions based on variable internal threshold values. To this end, some variables are discussed that could influence these threshold values, e.g. the confidence in a decision. Broder et al. (2014) postulated the information intrusion paradigm through which the various model classes can be tested empirically in probabilistic inference tasks without the need to create concrete models by changing the stimulus environments in such a way that both paradigms make opposite predictions about the measurable search, stop, and decision making behavior during the information gathering process. The Oil Drilling Task (Rieskamp & Otto, 2006) was adapted for this purpose. The first goal of the conceptual replication study is to replicate the findings of Söllner and Bröder (2016) that speak for the EA models. The second goal is to manipulate the stimulus environment through information costs in order to generate and investigate an adaptive use of decision strategies by implementing the variation in information costs within-subject instead of between-subject (Söllner & Bröder, 2016). The third goal is to expand the results of Söllner and Bröder (2016) through the confidence in decisions, as this is recorded as a further dependent variable in the theoretical literature. The results indicate that test subjects systematically varied their search and stopping behavior in connection with the predictions of the EA models and that the results of Söllner and Bröder (2016) could be replicated to a large extent. Individual threshold values could be estimated for a large part of the sample (75%). An adaptive use of different decision strategies could not be observed due to the low salience of the information costs. Nevertheless, the test subjects for whom a threshold value could be estimated adapted it based on the cost conditions. In addition, it was shown that the confidence varied systematically in connection with the validities and compatibilities of cues and that the assumption of variable threshold values, which are regulated by changes in confidence, thus appears plausible. The theoretical effects of these results show further predictive power through the EA models, which is why a uniform theory formulation for probabilistic inference tasks should refer to EA models in connection with confidence thresholds.
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... Behavioral scientists commonly agree that this trade-off is adaptive such that people adjust their effort and the amount of information they process depending on the respective context and the goals they want to achieve (Payne, Bettman, & Johnson, 1988;Simon, 1956). However, there is an ongoing discussion regarding how this adaptation takes place (Chater, Oaksford, Nakisa, & Redington, 2003;Lee & Cummins, 2004;Marewski & Schooler, 2011;Söllner & Bröder, 2016). ...
... In our model comparison we focus on the two most prominent theoretical frameworks that have been proposed to elucidate the processes underlying multi-attribute choice (Söllner & Bröder, 2016;Busemeyer, 2017). One framework assumes that decision makers adapt their behavior by applying qualitatively different cognitive strategies. ...
... Thus, the threshold defines how much information is integrated and by adjusting it, sequential sampling models can account for adaptive changes in behavior. This idea is sometimes, in analogy to the Toolbox metaphor, referred to as an Adjustable Spanner (Newell, 2005;Söllner & Bröder, 2016). ...
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Past research indicates that individuals respond adaptively to contextual factors in multi-attribute choice tasks. Yet it remains unclear how this adaptation is cognitively governed. In this paper, empirically testable implementations of two prominent competing theoretical frameworks are developed and compared across two multi-attribute choice experiments: The Adaptive Toolbox framework assuming discrete choice strategies and the Adjustable Spanner framework assuming one comprehensive adaptive strategy. Results from two experiments indicate that in the environments we tested, in which all cue information was presented openly, the Toolbox makes better predictions than the Adjustable Spanner both in- and out-of-sample. Follow-up simulation studies indicate that it is difficult to discriminate the models based on choice outcomes alone but allowed the identification of a small sub-set of cases where the predictions of both models diverged. Our results suggest that people adapt their decision strategies by flexibly switching between using as little information as possible and use of all of the available information.
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... mechanism models. He has interpreted many of his own findings in terms of repertoires of strategies (e.g., Bröder, 2012), but has also found critical evidence challenging the multistrategy view (e.g., Glöckner & Bröder, 2011Söllner & Bröder, 2016). Purposefully combining different perspectives would, so we hoped, help us to sketch out a more or less balanced (or at least multi-faceted) view on the strategy selection problem. ...
... 2005; Söllner & Bröder, 2016), identifying ideal environments for take-the-best or a compensatory strategy is, in A. G.'s and A. B.'s view, tantamount of identifying situations in which narrow or wide thresholds are optimal, respectively. 14 (4) Fourth, there have been attempts to determine, empirically, which class of models better captures human decision making descriptively. ...
... For example, as just pointed out, a non-compensatory [or compensatory] heuristic can be imitated by narrow [or wide] boundaries in evidence accumulation (e.g., Lee & Cummins, 2004), and the decisions of different heuristics can also be produced by setting appropriate sensitivity parameters in a network model . Only with specific experimental setups, so some argue (Söllner & Bröder, 2016), evidence in favor of one of the models can be obtained. Others-including one of us (Marewski, Schooler, & Gigerenzer, 2010) -point out that specific experimental set-ups are not enough, and that certain experimental designs (e.g., those that try to unconfound variables) can, at times, make the identification of cognitive mechanisms even harder, an argument that can be traced back, for instance, to the environmentally minded Egon Brunswik. ...
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Organisms must be capable of adapting to environmental task demands. Which cognitive processes best model the ways in which adaptation is achieved? People can behave adaptively, so many frameworks assume, because they can draw from a repertoire of decision strategies, with each strategy particularly fitting to certain environmental demands. In contrast to that multi-mechanism assumption, competing approaches posit a single decision mechanism. The juxtaposition of such single-mechanism and multi-mechanism approaches has fuelled not only much theory-building, empirical research, and methodological developments, but also many controversies. This special issue on “Strategy Selection: A Theoretical and Methodological Challenge” sheds a spotlight on those developments. The contribution of this introductory article is twofold. First, we offer a documentation of the controversy, including an outline of competing approaches. Second, this special issue and this introductory article represent adversarial collaborations among the three of us: we have modeled adaptive decision making in different ways in the past. Together, we now work on resolving the controversy and point to five guiding principles that might help to improve our models for predicting adaptive behavior. Copyright © 2018 John Wiley & Sons, Ltd.
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... Studies comparing multistrategy models (like the adaptive toolbox) with singlestrategy models (like the parallel constraint model from which the iCodes model originated) provide evidence to support the validity of both models. Nevertheless, single-strategy models are more effective than multiple-strategy models in a number of ways, including economic efficiency and the predictive power of the decision (Glöckner et al., 2014;Söllner & Bröder, 2016). However, it is noteworthy that these studies did not investigate selection decisions in sports contexts. ...
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The iCodes model by Jekel et al. (2018) states predictions about the information search in decision-making processes. Previous research validated the attraction search effect in various nonsport scenarios. This investigation explores coaches’ decision making in athlete selection contexts using the iCodes model’s main hypotheses. Hypothesis 1 states that the probability of coaches searching for a particular criterion regarding an athlete increases with the criterion’s validity. Hypothesis 2 states that when information is available, information search will be directed toward the more attractive athlete as an option (i.e., the attraction search effect). Moreover, a third hypothesis is added: The information search process differs between coaches from different sports. Seventy-five coaches were confronted with 52 option-information boards. In each trial, coaches opened athletes’ criteria and selected one athlete as “most talented.” After completing all trials, coaches ranked the criteria regarding their validity for athlete selection. Results showed evidence in favor of Hypotheses 1 and 3 but indeterminate regarding Hypothesis 2. Overall, this study presents an extension of the iCodes model and contributes to a better understanding of the underlying mechanisms of the coach’s eye.
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... 3 Several studies have shown that decision makers classified as users of TTB based on their decisions do not completely ignore attributes after the first discriminating attribute, as this information seems to affect response times and subjective confidence Dummel et al., 2016;Söllner & Bröder, 2016). 4 That search costs are important for strategy selection is also supported in studies by Platzer et al. (2014) and Platzer and Bröder (2012) on decisions from memory. ...
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... Newell (2005) argued that models based on efficient adaptation of a limited capacity system to the environment tend to explain the same essential phenomena and largely to mimic one another. Indeed, these models are hard to tease apart empirically, although contrasting predictions have been examined regarding context effects (Tsetsos, Usher, & Chater, 2010), termination of information search (Söllner & Bröder, 2016) and speedaccuracy trade-offs (Teodorescu & Usher, 2013). Our experiments do not aim to disentangle these models, but rather to provide an empirical "reality check" on this class of models. ...
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... Other disciplines elaborate toolbox metaphors in various ways. Psychologists have proposed an "adaptive toolbox" model of decision making, in which people use different decision-making rules depending on their circumstances, rather than simply applying the same rule with different inputs (Bröder and Newell, 2008;Söllner and Bröder, 2016). Anderson (2007) proposes we think of management theories as a "toolbox" from which managers can draw concepts relevant to the specific demands they face, without seeking one over-arching management theory or principle. ...
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... Hausmann and Läge (2008) showed that an individually assessed evidence threshold could predict subsequent information search and stopping behaviour better than various decision heuristics proposed in the literature. Similarly, Söllner and Bröder (2016) showed that evidence thresholds varied considerably between individuals, and that thresholds were also sensitive to extrinsic factors like information acquisition costs. ...
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The Adaptive Decision Maker
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Chapter
  • Apr 2012
"More information is always better, and full information is best. More computation is always better, and optimization is best." More-is-better ideals such as these have long shaped our vision of rationality. Yet humans and other animals typically rely on simple heuristics to solve adaptive problems, focusing on one or a few important cues and ignoring the rest, and shortcutting computation rather than striving for as much as possible. In this book, we argue that in an uncertain world, more information and computation are not always better, and we ask when, and why, less can be more. The answers to these questions constitute the idea of ecological rationality: how we are able to achieve intelligence in the world by using simple heuristics matched to the environments we face, exploiting the structures inherent in our physical, biological, social, and cultural surroundings.
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