The influence of information redundancy on probabilistic inferences

Dieckmann, Anja; Rieskamp, Jörg

doi:10.3758/bf03193511

Cited by 103 publications

(86 citation statements)

References 49 publications

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“…This difference in satisficing strategy likely represents an adaptive shift in information integration to accommodate the less certain nature of cue information and much higher time pressure in the present task. First, in order to implement the take-the-best approach, it is crucial for the decision maker to be confident in their knowledge of the exact rankings of cue validities; in fact, the majority of previous work on this heuristic has provided participants with explicit cue validities on each trial (e.g., Bröder, 2000Bröder, , 2003Dieckmann & Rieskamp, 2007;Payne et al, 1988Payne et al, , 1996Rieskamp & Hoffrage, 2008;Rieskamp & Otto, 2006). Moreover, it has been shown that experts who have better knowledge of cue validities than novices are more likely to adopt the take-the-best heuristic (Garcia-Retamero & Dhami, 2009;Pachur & Marinello, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…Many of the previous studies that documented the use of lexicographic heuristics under satisficing pressures have used noncompound stimuli (e.g., Bröder, 2000;Dieckmann & Rieskamp, 2007;Payne, Bettman, & Johnson, 1988;Rieskamp & Otto, 2006), presenting cues independently, for example in the form of an information matrix. In Experiments 1 and 2, we took a departure from this approach and used compound cue stimuli, which integrated all cue dimensions to construct a single object.…”

Section: Experiments 3: Satisficing Using Noncompound Cues Under Timementioning

confidence: 99%

“…By contrast, when dealing with unfamiliar situations, people appear to take into account the informational structure of the decision problem, such as the relative values and intercorrelations between different sources of information and the cost of acquiring new information, in adopting decision heuristics (Bröder, 2000(Bröder, , 2003Dieckmann & Rieskamp, 2007;Rieskamp & Otto, 2006). For example, one way to simplify a decision problem is to evaluate one piece of information (i.e., one cue) at a time, starting from what is considered the most important to less important cues, based on a subjective rank order.…”

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confidence: 99%

“…Laboratory studies that have documented participants' spontaneous use of this cue-based satisficing strategy have typically provided subjects with a set of cues of varying, explicitly known values, and then assessed the manner in which subjects combine the different cues as a function of satisficing pressures (e.g., redundant cue information, information costs; e.g., Bröder, 2003;Dieckmann & Rieskamp, 2007;Newell, Weston, & Shanks, 2003;Payne, Bettman, & Johnson, 1988;Rieskamp, 2006;Rieskamp & Otto, 2006). In addition, a majority of these studies adopted a serial information search paradigm (Payne et al, 1988), which enables easy tracking of choice patterns but also constrains behavior by hindering quick comparisons of multiple pieces of information (Glöckner & Betsch, 2008).…”

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confidence: 99%

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Satisficing in split-second decision making is characterized by strategic cue discounting.

Oh¹,

Beck²,

Zhu³

et al. 2016

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Much of our real-life decision making is bounded by uncertain information, limitations in cognitive resources, and a lack of time to allocate to the decision process. It is thought that humans overcome these limitations through satisficing, fast but "good-enough" heuristic decision making that prioritizes some sources of information (cues) while ignoring others. However, the decision-making strategies we adopt under uncertainty and time pressure, for example during emergencies that demand split-second choices, are presently unknown. To characterize these decision strategies quantitatively, the present study examined how people solve a novel multicue probabilistic classification task under varying time pressure, by tracking shifts in decision strategies using variational Bayesian inference. We found that under low time pressure, participants correctly weighted and integrated all available cues to arrive at near-optimal decisions. With increasingly demanding, subsecond time pressures, however, participants systematically discounted a subset of the cue information by dropping the least informative cue(s) from their decision making process. Thus, the human cognitive apparatus copes with uncertainty and severe time pressure by adopting a "drop-the-worst" cue decision making strategy that minimizes cognitive time and effort investment while preserving the consideration of the most diagnostic cue information, thus maintaining "good-enough" accuracy. This advance in our understanding of satisficing strategies could form the basis of predicting human choices in high time pressure scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Experiments 3: Satisficing Using Noncompound Cues Under Timementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Satisficing in split-second decision making is characterized by strategic cue discounting.

Oh¹,

Beck²,

Zhu³

et al. 2016

Journal of Experimental Psychology: Learning, Memory, and Cogni

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“…In predicting new data, take-the-best is often as good or better than models that take into account more information if the cues are highly intercorrelated with each other (Dieckmann & Rieskamp, 2007), that is, if there is much redundancy in the environment. To illustrate, imagine the extreme case of cue intercorrelations of 1.…”

Section: Betting On One Good Cue: the Take-the-best Heuristicmentioning

confidence: 99%

Five Principles for Studying People’s Use of Heuristics

Marewski¹,

Schooler²,

Gigerenzer³

2010

Acta Psychologica Sinica

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Abstract:The fast and frugal heuristics framework assumes that people rely on an adaptive toolbox of simple decision strategies-called heuristics-to make inferences, choices, estimations, and other decisions. Each of these heuristics is tuned to regularities in the structure of the task environment and each is capable of exploiting the ways in which basic cognitive capacities work. In doing so, heuristics enable adaptive behavior. In this article, we give an overview of the framework and formulate five principles that should guide the study of people's adaptive toolbox. We emphasize that models of heuristics should be (i) precisely defined; (ii) tested comparatively; (iii) studied in line with theories of strategy selection; (iv) evaluated by how well they predict new data; and (vi) tested in the real world in addition to the laboratory. Key words: fast and frugal heuristics; experimental design; model testing As we write this article, international financial markets are in turmoil. Large banks are going bankrupt almost daily. It is a difficult situation for financial decision makers -regardless of whether they are lay investors trying to make small-scale profits here and there or professionals employed by the finance industry. To safeguard their investments, these decision makers need to be able to foresee uncertain future economic developments, such as which investments are likely to be the safest and which companies are likely to crash next. In times of rapid waves of potentially devastating financial crashes, these informed bets must often be made quickly, with little time for extensive information search or computationally demanding calculations of likely future returns. Lay stock traders in particular have to trust the contents of their memories, relying on incomplete, imperfect Humans are not omniscient. We do not come equipped with the ability to run computationally demanding calculations quickly in our heads. Rather, we make decisions under the constraints of limited information processing capacity, knowledge, and time -be they about the likely performance of stocks, which movie to see at the multiplex, whom to match up with in a speed-dating session, or whether to hospitalize a patient who has registered at the emergency room reception. According to the fast and frugal heuristics research program (for recent reviews, see Gigerenzer & Brighton, 2009; press a), humans can nevertheless make such decisions successfully because they can rely on a repertoire of simple decision strategies, or heuristics. These simple rules of thumb can often perform well even under the constraints of limited knowledge, time, and information-processing capacity. They do so by exploiting the structure of information in the environment in which a decision maker acts and by building on the ways cognitive capacities work, such as the speed with which the human memory system retrieves information. Together, these simple rules of thumb form an adaptive toolbox of

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How Affect Shapes Risky Choice: Distorted Probability Weighting Versus Probability Neglect

Suter

Pachur

Hertwig

2015

Behavioral Decision Making

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People's choices between prospects with relatively affect-rich outcomes (e.g., medical side effects) can diverge markedly from their choices between prospects with relatively affect-poor outcomes (e.g., monetary losses). We investigate the cognitive mechanisms underlying this "affect gap" in risky choice. One possibility is that affect-rich prospects give rise to more distortion in probability weighting. Another is that they lead to the neglect of probabilities. To pit these two possibilities against each other, we fitted cumulative prospect theory (CPT) to the choices of individual participants, separately for choices between options with affect-rich outcomes (adverse medical side effects) and options with affect-poor outcomes (monetary losses); additionally, we tested a simple model of probability neglect, the minimax rule. The results indicated a qualitative difference in cognitive mechanisms between the affect-rich and affect-poor problems. Specifically, in affect-poor problems, the large majority of participants were best described by CPT; in affect-rich problems, the proportion of participants best described by the minimax rule was substantially higher. The affect gap persisted even when affect-rich outcomes were supplemented by numerical information, thus providing no support for the thesis that choices in affect-rich and affect-poor problems diverge because the information provided in the former is nonnumerical. Our findings suggest that the traditional expectation-based framework for modeling risky decision making may not readily generalize to affect-rich choices.Additional supporting information may be found in the online version of this article at the publisher's web-site. key words risky choice; affect; probability weighting; prospect theory; minimax rule 1 Note that our distinction between "affect-rich" and "affect-poor" choices and outcomes is an ordinal rather than an absolute one. Even affect-poor outcomes are not completely devoid of affective quality. MaterialsParticipants were presented with a total of four tasks: a monetary evaluation task, two choice tasks (involving affect-poor and affect-rich problems, respectively), and an affective evaluation task. In the monetary evaluation task, participants were first asked to rank the following 12 side effects from most to least unpleasant: fatigue,

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The influence of information redundancy on probabilistic inferences

Cited by 103 publications

References 49 publications

Satisficing in split-second decision making is characterized by strategic cue discounting.

Satisficing in split-second decision making is characterized by strategic cue discounting.

Five Principles for Studying People’s Use of Heuristics

How Affect Shapes Risky Choice: Distorted Probability Weighting Versus Probability Neglect

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